Characterizing chemical signaling between engineered “microbial sentinels” in porous microplates

Abstract Living materials combine a material scaffold, that is often porous, with engineered cells that perform sensing, computing, and biosynthetic tasks. Designing such systems is difficult because little is known regarding signaling transport parameters in the material. Here, the development of a porous microplate is presented. Hydrogel barriers between wells have a porosity of 60% and a tortuosity factor of 1.6, allowing molecular diffusion between wells. The permeability of dyes, antibiotics, inducers, and quorum signals between wells were characterized. A “sentinel” strain was constructed by introducing orthogonal sensors into the genome of Escherichia coli MG1655 for IPTG, anhydrotetracycline, L‐arabinose, and four quorum signals. The strain’s response to inducer diffusion through the wells was quantified up to 14 mm, and quorum and antibacterial signaling were measured over 16 h. Signaling distance is dictated by hydrogel adsorption, quantified using a linear finite element model that yields adsorption coefficients from 0 to 0.1 mol m−3. Parameters derived herein will aid the design of living materials for pathogen remediation, computation, and self‐organizing biofilms.

The manuscript by Vaiana et. al. shows the design and construction of a porous microplate with and its use as living material. In particular, they apply thy demonstrate that it is possible to use their platform to study to detect several small molecules orthogonally, cell to cell communication in the form of quorum sensing, the detection and performance of antibiotics and antimicrobial activity of bacteria. The manuscript is written in a clear and easy to understand style, the methodology properly described, and the results support the conclusions drawn from them. However, the authors should address several points before the manuscript is acceptable for publication in Molecular System Biology: Major points: The term absorption is used several times probably referring to adsorption, for example in line 244: 'adsorption coefficients ranging from 0 mol m-3 (no absorption)' Authors should define the interaction between the diffusing molecules and the porous microplate and use the right term.
Interactions between the solute and the HEMA-EDMA microplate walls: the authors state that the diffusion of rhodamine B is impaired by its adsorption in the polymer. However, the Fig 1 g. and h. show the walls stained to a higher extent when using cotton blue than with rhodamine B. Authors should consider other interactions with the polymer besides adsorption such as electrostatic attraction repulsion to explain the differences in the polymer-dye interactions. Besides that, as it seems from the graphs on these figures, the concentration of dye added in the central well is different for both dyes, being higher that for cotton blue. Authors should justify that difference or use the same concentration for both of them.
Authors should justify why they have used a simulation of the diffusion and adsorption of the inducers through the microplate instead a direct measurement of the concentration of the inducers in each well. They should also consider to experimentally validate the profiles of their model with the direct measurement of their concentration besides the indirect method of predicting the RPU profile and comparing it with the observed RPU of each output strain. This might be a more direct way to estimate the value of KL.
Authors should explain in more detail the reason behind the selection of the concentration of each inducer in the mix introduced in the center well for the experiment in Figure 3. Is it based in the detection limits found in the dose-response curves? Authors should discuss about the importance, or not, of the concentration of the inducer in the center well on its diffusion and adsorption in the walls of the microplate.
Minor points: • In the abstract use signal/signaling molecules instead quorums? Review this for the entire paper, for example in line 159 use 'signaling molecules sensor' instead of 'quorum sensors' • Lines 58 and 62, use the same nomenclature, either um or microns • Line 62, write Escherichia Coli since it is the first time mentioned in the text. The same applies for all the other species mentioned in the manuscript • Figure 1 h: re-size the y axis of the graph • Figure 4 d: check the labels of the y axes, the 0.1 should be 1? • Figure 4 e and experiment with the strain producing Microcin A: in the text authors mention that they "observed a greater than 50 % cell death at a distance of one well away from the center microcin producing culture", however in the graph it appears that the inhibition was around 90% for all the cases. Authors should check and correct this discrepancy. • Line 446: specify the concentration of the dye.
• Please indicate the mutations found on the optimized activators. • In table 2, the partition coefficient is named as KL, while in equation 2 us just K. Please unify.
This manuscript provides a clear description of a new materials platform technology for studying cell-cell signaling through porous matrices and they describe the development of an expanded set of transcriptional regulators that can be used orthogonally from one another. The authors present a nice set of proof of concept experiments for evaluating the movement of a variety of molecules through this material, ranging from chemical dyes to cell-cell signals to synthetic chemical inducers to antibiotics. These experiments show how chemical concentrations vary with distance and time, and a quantitative model is used to fit an analyze the data that considers effective diffusivity and adsorption isotherm models. In general, the manuscript is well written, the data is clearly presented, and the conclusions are supported by the data. The technologies presented will be of interest to the readership of the journal.
Additional comments: Long chain AHL data. The modeling seems to only consider diffusivity and adsorption, and a supplemental figure is presented suggesting that these parameters are largely responsible for the observed trends. Long chain AHLs have been shown to exhibit pHdependent hydrolysis (DOI, 10.1128/IAI.70.10.5635-5646.2002) which has been shown to be modified by porous environmental materials (DOI, 10.1021/acsomega.6b00085). These pH dependent kinetics are relatively fast on the time scale of the experiments presented, and it would be good to note that some of the data points shown in Figure S9 may be influenced by such processes.
We agree with the reviewer's point regarding the effects of pH-dependent kinetics on the half-life of homoserine lactones due to a ring-opening hydrolysis transformation, or "lactonolysis." We have added the following paragraph to discuss the potential impact of lactonolysis on our observations at Line 303: Supplementary Fig. 9 and Fig. 4A, in addition to solubility and hydrogel adsorption."

"It is important to note that other environmental factors may influence the bioavailability of signaling molecules in porous materials. For instance, homoserine lactones will undergo lactonolysis, a ring opening transformation that is dependent on environmental factors like pH and temperature, and that is stabilized to different degrees based on acyl chain length (Yates et al, 2002). In liquid culture, the half-life of OC6 can decreases from 32 h in LB media at pH = 6.0, to ~ 9 h in M9 media at pH = 7 (Politi et al, 2014). The implications of lactonolysis is apparent on environmentally relevant quorum signaling, such as the changes in homoserine lactone accumulation in biocharred soils (Gao et al, 2016b). Given that lactonolysis kinetics occur on timescales relevant to our studies, these processes may influence the observed signaling distances presented in
Abstract: "quorums" is not clear; suggest changing to autoinducers or quorum signal We agree, and "quorums" has been replaced by "quorum signals" in the abstract.

29th Dec 2021 1st Authors' Response to Reviewers
Reviewer #2: Vaiana et al. present a very interesting study on the development of living materials composed of a bespoke porous microplate with hydrogel (methacrylate) barriers intercalated between wells that manage transport phenomenon, which contain engineered E. coli chassis cells. The authors developed an engineered E. coli chassis cell with biosensing capabilities to IPTG, anhydrotetracycline, L-arabinose, and four quorums (luxR, cinR, lasR, and rpaR) -fashioned after the previously reported "Marionette Strain". Transport characteristics of dyes, antibiotics, inducers, and quorums between wells were assessed. The resulting "sentinel" strain's response to inducer diffusion through the wells was quantified up to 14 mm, and quorum and antibacterial signaling were measured over 16 h. The author's assess how adsorption impacts the transport of each molecule differently, and deduce an adsorption coefficient for each inducer with respect to the methacrylate scaffold using a linear adsorption model.
In general, I enjoyed reading this manuscript and found the results to be of high-quality. At times, I was somewhat puzzled by seemingly disconnected objectives and the purpose of certain aspects of the work -however I believe this could be remedied by additional discussion rather than by more experiments. All things considered, this work should be of significant interest to the MSB community and I am supportive of publication after some revision.
Comment: To test the permeability of the microplate walls, the authors measured the effective diffusion of the visible dyes cotton blue and rhodamine B from the center well. Why were these two dyes selected (i.e., what are the dyes physical and chemical properties, and justification for use)?
These dyes were selected because they have similar Molecular Weights (MW) but different water solubilities. The manuscript text has been updated to include this clarification at line 162.
Comment: Figure 1d can the authors include error bars. As indicated, each data point represents a single device and each condition was measured three times.
We refer to a specific decanol / cyclohexanol ratio as a "condition." There are three devices fabricated per condition, and one measurement was taken for each of those devices. All collected data are plotted in Figure 1d, and therefore do not warrant error bars. This has been clarified in the Figure 1d legend.
Comment: The authors put considerable effort into engineering a sensor strain that responds orthogonally to 7 small molecules. Why? Were the authors interested in facilitating the detection of a diverse collection of small molecules -i.e., with different physicochemical properties? Will this chassis be used for a specific purpose in future applications?
Our original intent was to study layers of multi-input induction and sequential auto-induced quorum signaling cascades using the microplate, but the effects of signal adsorption were too strong to achieve this scope. We do hope this strain can be applied toward these studies in the future.
Comment: Please consider replacing "Henry's law" with "Henry's adsorption isotherm". Also, what was the purpose of the simulations for transport through the microplate of the seven inducers that activate the sensor strain (Fig. 3a)? Will this aid in the design of new materials?
We agree with the change from "Henry's law" to "Henry's adsorption isotherm" and have made this change in the manuscript at line 238. The purpose of the simulations was to assign a quantitative parameterin this case adsorption coefficientto explain the transport of different biological signaling molecules. This quantification, we hope, will in fact aid in the design of new materials that may rely on biological signaling cascades, so that signaling distances can be better predicted.
Comment: The authors surmise that the predominant factor determining the effective diffusion distance of each molecule is the propensity of the hydrogel matrix to adsorb that inducer, and this adsorption is related to the solubility of the molecule. Can the authors propose what alternate materials would potentially mitigate adsorption?
We have discussed alternative hydrogel materials with varying properties in the introduction. In addition, we have included the following statement in the discussion referencing some work done in alginate-acrylamides polymers that has shown less absorption of biological inducers at line 376:

"Some applications may warrant the use of porous materials that allow inducers to pass unhindered, such as alginate, acrylamide, and agarose (Liu et al., 2017; Liu et al., 2018)."
Reviewer #3: The manuscript by Vaiana et. al. shows the design and construction of a porous microplate with and its use as living material. In particular, they apply thy demonstrate that it is possible to use their platform to study to detect several small molecules orthogonally, cell to cell communication in the form of quorum sensing, the detection and performance of antibiotics and antimicrobial activity of bacteria. The manuscript is written in a clear and easy to understand style, the methodology properly described, and the results support the conclusions drawn from them. However, the authors should address several points before the manuscript is acceptable for publication in Molecular System Biology: Major points: The term absorption is used several times probably referring to adsorption, for example in line 244: 'adsorption coefficients ranging from 0 mol m-3 (no absorption)' Authors should define the interaction between the diffusing molecules and the porous microplate and use the right term.

We acknowledge that the proper term is "adsorption" and we have corrected the text accordingly.
Interactions between the solute and the HEMA-EDMA microplate walls: the authors state that the diffusion of rhodamine B is impaired by its adsorption in the polymer. However, the Fig 1 g. and h. show the walls stained to a higher extent when using cotton blue than with rhodamine B. Authors should consider other interactions with the polymer besides adsorption such as electrostatic attraction repulsion to explain the differences in the polymer-dye interactions. Besides that, as it seems from the graphs on these figures, the concentration of dye added in the central well is different for both dyes, being higher that for cotton blue. Authors should justify that difference or use the same concentration for both of them.
Our initial experiment varied the starting concentration of dye slightly; 12.5 mM for methyl blue and 15.7 mM for rhodamine B. However, we do agree that there was no justification for this difference, therefore we repeated the methyl blue diffusion experiment with a starting concentration of 15.7 mM, and we have added the new data to Figure 1 with the axes of Fig 1G and H normalized. We can now directly compare the results of the two dyes. The fact that the center well was nearly depleted of rhodamine suggests that the adsorption of rhodamine to the hydrogel was significantly more than that of methyl blue.
We agree that electrostatic interactions play a role in molecule transport and we have added the following lines and additional references in the discussion to acknowledge this point at line 372:

"Environmental factors like pH and temperature play a role in dictating the solute interaction with the matrix, which could include attractive or repulsive electrostatics, hydrogen bonding, and hydrophobic effects (Hernandez-Martínez et al, 2018; Kudaibergenov et al, 2016). Our simplified model summarizes these forces as a single "adsorption coefficient," acknowledging that the entirety of these interactions has not been accounted for."
Authors should justify why they have used a simulation of the diffusion and adsorption of the inducers through the microplate instead a direct measurement of the concentration of the inducers in each well. They should also consider to experimentally validate the profiles of their model with the direct measurement of their concentration besides the indirect method of predicting the RPU profile and comparing it with the observed RPU of each output strain. This might be a more direct way to estimate the value of KL.
While we agree that a direct measurement of the concentration of each molecule would be a direct way to calculate K H , through the dose-response curves we have demonstrated that we can use the engineered sentinel strain as a tool to measure trace amounts of each inducer molecule in the small volume within each well. It would be technically challenging to accurately quantify these amounts by other means (mass spectrometry for example). Furthermore, it is ultimately the biological response that we are interested in quantifying, as it serves as a proof of concept for the design of a "living material" with chemical sensing capabilities, and therefore we chose to rely on this response to define the adsorption parameters of the model.
Authors should explain in more detail the reason behind the selection of the concentration of each inducer in the mix introduced in the center well for the experiment in Figure 3. Is it based in the detection limits found in the dose-response curves? Authors should discuss about the importance, or not, of the concentration of the inducer in the center well on its diffusion and adsorption in the walls of the microplate.

"We concentrated the inducers to 20 times the maximum concentration as determined by the dose response curves. These concentrations were chosen in an effort to ensure a detectable signal would be achieved several wells away from the center once the inducer diffuses away, but were within the water solubility limits of the inducer molecules."
Minor points: • In the abstract use signal/signaling molecules instead quorums? Review this for the entire paper, for example in line 159 use 'signaling molecules sensor' instead of 'quorum sensors' We changed quorums to "quorum signals" in the abstract in response to this comment as well as comments from Reviewer #1.
• Lines 58 and 62, use the same nomenclature, either um or microns We corrected this by using "µm" instead of micron throughout the manuscript.
• Line 62, write Escherichia Coli since it is the first time mentioned in the text. The same applies for all the other species mentioned in the manuscript We have corrected this throughout the manuscript.
• Figure 1 h: re-size the y axis of the graph We have re-sized the y axis in Figure 1H. • Figure 4 e and experiment with the strain producing Microcin A: in the text authors mention that they "observed a greater than 50 % cell death at a distance of one well away from the center microcin producing culture", however in the graph it appears that the inhibition was around 90% for all the cases. Authors should check and correct this discrepancy.
We observe ~ 90 % inhibition due to diffusion of the antibiotics that are depicted in Figure 4d; however, the inhibition resulting from the Microcin producer is correctly annotated as greater than ~ 50% as indicated in Figure 4e.
• Line 446: specify the concentration of the dye. We have updated the text to indicate the dye concentration.
• Please indicate the mutations found on the optimized activators. We have added the specific sequences of the original and optimized promoters and activators in Appendix Table S1 and S2.
• In table 2, the partition coefficient is named as KL, while in equation 2 us just K. Please unify. We actually chose to label the partition coefficient K H in both locations; to delineate from K used in the Hill coefficients in Table 1

, and because it is derived from Henry's Adsorption Isotherm (as opposed to the Langmuir Isotherm which often uses a constant annotated K L ). 1st Revision -Editorial Decision
Thank you for sending us your revised manuscript. We have now evaluated your revised study and we think that the performed revisions have satisfactorily addressed the issues raised by the reviewers. As such I am glad to inform your that we can soon proceed with formally accepting the study for publication. Before we can proceed with the formal acceptance, we would also ask you to address some minor editorial issues listed below.

24th Jan 2022 2nd Authors' Response to Reviewers
The authors have made all requested editorial changes. Do the data meet the assumptions of the tests (e.g., normal distribution)? Describe any methods used to assess it.
Is there an estimate of variation within each group of data?

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